Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

Yang, Rui; Liu, Yongbao; Yu, Youhong; He, Xiuli; Li, Hongsong

doi:10.1016/j.egyr.2021.08.120

Cited by 37 publications

(8 citation statements)

References 22 publications

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“…The traditional genetic algorithm often falls into the problem of local optimal solutions, which limits its ability to explore the global search space [7] . Therefore, on the basis of the genetic algorithm, this paper improves the steps of coding, selection, and mutation, aiming at enhancing the searchability, speeding up the convergence speed, and effectively overcoming the problem of falling into the local optimal solutions in the iterative process [8,9] . The improved genetic algorithm is optimized in the following three aspects.…”

Section: Bldc Simulation Modelmentioning

confidence: 99%

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

Liu,

Zhou,

Cao

2024

J. Phys.: Conf. Ser.

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This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

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Section: Bldc Simulation Modelmentioning

confidence: 99%

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

Liu,

Zhou,

Cao

2024

J. Phys.: Conf. Ser.

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“…Hekimoglu et al 26 proposed the atomic Search Optimization (ASO) algorithm and its modified version to determine the control parameters of the PID controller for motor speed. To enhance the control performance of gas turbines, a hybrid control technique based on a modified particle swarm optimization algorithm (PSO) and cuckoo search algorithm (HIPSO_CS) is proposed by Yang et al 27 for PID parameter adjustment. The simulation outcomes presented that the gas turbine controlled by the fuzzy PID controller based on HIPSO_CS has a fast system response and good control stability.…”

Section: Related Workmentioning

confidence: 99%

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

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“…In addition to devices from micro-controllers (Yang et al, 2021) that have simple circuits, but if the data is generated from sensors such as ultrasonic sensors (Lawson et al, 2022), temperature and humidity sensors (Gancliev et al, 2019), water level detection sensors, CO, NO dangerous gas detection sensors and other sensors. Much of the research uses Arduino, esp8266 the resulting data is further processed.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers

Saputra

Hindarto

Haryono

2023

SinkrOn

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In processing data science, data is needed as input. Sometimes the data needed does not exist in public data, this is where the purpose of this research is made. The acquisition process is so important to process information into data. After that, the data is processed to make a decision. Micro-controller in controlling conditions, such as temperature, and humidity are very common devices, and a lot of research has been done. Sometimes discussing it only shows how to create a series and save it on online platforms, such as firebase, tinger.io, and many others online platforms. So that the process of storing data on an external or online platform is an advantage for platform providers, where platform providers do not need to do business and get data for free. This is without realizing the researchers who have produced a micro-controller device. Many platforms for storing data range from hardware and software devices. Some devices are paid or open source. This research uses software tools that are open source. Because using open source-based tools it will be easy to develop and for further research purposes. The development of the following research by entering code into a micro-controller system or what is called an embedded system. Data is a very valuable asset. Because data is one of the most important components in processing in data science. And it is better to take care of the data logger. This research uses Arduino as a micro-controller and ultrasonic distance sensor and potentiometer

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Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

Cited by 37 publications

References 22 publications

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers

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